Alireza Ghazavi Khorasgani

—This paper characterizes the optimal capacity-distortion (C-D) tradeoff in an optical point-to-point system with single-input single-output (SISO) for communication and single-input multiple-output (SIMO) for sensing within an integrated sensing and communication (ISAC) framework. We consider the optimal rate-distortion (R-D) region and explore several inner (IB) and outer bounds (OB). We introduce practical, asymptotically optimal maximum a posteriori (MAP) and maximum likelihood estimators (MLE) for target distance, addressing nonlinear measurement-to-state relationships and non-conjugate priors. As the number of sensing antennas increases, these estimators converge to the Bayesian Cramér-Rao bound (BCRB). We also establish that the achievable rate-Cramér-Rao bound (R-CRB) serves as an OB for the optimal C-D region, valid for both unbiased estimators and asymptotically large numbers of receive antennas. To clarify that the input distribution determines the tradeoff across the Pareto boundary of the C-D region, we propose two algorithms: i) an iterative Blahut-Arimoto algorithm (BAA)-type method, and ii) a memory-efficient closed-form (CF) approach. The CF approach includes a CF optimal distribution for high optical signal-to-noise ratio (O-SNR) conditions. Additionally, we adapt and refine the deterministic-random tradeoff (DRT) to this optical ISAC context.

This study introduces a novel approach for energy-efficient resource allocation in millimeter-wave networks, assisted by multiple intelligent reflecting surfaces (IRS). The proposed framework optimizes the dynamic ON/OFF control and phase shifts of IRS elements, along with beamforming (BF) at access points (AP), under practical constraints. Unlike existing methods, our model enhances energy efficiency (EE) by optimizing a fixed number of ON IRS elements. We present innovative algorithms, including a modified nested fractional programming (NFP) for BF and a simulated annealing (SA)-type algorithm for phase shift and element selection. Our results demonstrate a 6.5-fold improvement in EE under a realistic scenario compared to benchmark, highlighting the effectiveness of our approach as a crucial strategy for future 6G networks.

In this paper, an unmanned aerial vehicle (UAVs)-assisted visible light communication (VLC) has been considered which has two tiers: UAV-to-centroid and device-to-device (D2D). In the UAV-to-centroid tier, each UAV can simultaneously provide communications and illumination for the centroids of the ground users over VLC links. In the D2D tier, the centroids retransmit received data from UAV over D2D links to the cluster members. For network, the optimization problem of joint user association and deployment location of UAVs is formulated so as to maximize the received data, satisfy illumination constraints, and also the user cluster size. An iterative algorithm is first proposed to transform the optimization problem into a series of two interdependent sub problems. Following the smallest enclosing disk theorem, a random incremental construction method is designed to find the optimal UAV locations. Then, inspired by unsupervised learning method, a clustering algorithm to find a suboptimal user association is proposed. Our simulation results show that the proposed scheme on average guarantees the users brightness 0.3 microwatt more than their threshold requirements. Moreover, the received bitrate plus number of D2D connected users under our proposed method is 55.0% more than the scenario in which we do not optimize UAV location.